The objectives of the current research program are to understand how people learn causal and conceptual knowledge, and how causal and conceptual knowledge interact with each other. In particular, the proposed project examines two types of concepts that would be influential in causal induction. The first type is people's concepts about structural characteristics of complex causal relations. One such example is the conditional independence assumption in Bayesian Networks, which states that in a causal chain of X causing Y and Y causing Z, X is not predictive of Z once the value of Y is known. Given this assumption, the contingency between X and Z in the above causal chain becomes the product of the contingency between X and Y and the contingency between Y and Z. The first specific aim is to test whether people follow this product rule when they are presented only with piecemeal covariations (e.g., covariation between X and Y, and covariation between Y and Z) and combine them into a causal chain. The second type of prior concept that would be influential in causal induction is knowledge people have about specific events or objects. It is hypothesized that during sequential presentations of covariation information, people initially form a hypothesis about causal relations between specific events presented during the learning phase and interpret later data in light of this initial hypothesis. Consequently, people would be more influenced by data presented early on during learning of a causal relation than by data presented later in the same learning phase, resulting in a primacy effect. Thus, an overarching theme in this proposal is that people apply prior concepts when learning new causal relations both at an abstract level (e.g., constraints imposed on causal structures regardless of the content of specific events) as well as at a specific level (e.g., concepts about causal efficacy of specific events). Understanding causal and conceptual knowledge has important health implications because laypeople as well as clinicians often form causal models for disorders and their treatments, and these models greatly influence health-related decisions involving preventive actions and treatment plans. The aim is to go beyond mere demonstrations of the use of background knowledge in causal induction and to examine the specific nature of processes in which background concepts influence causal induction. [unreadable] [unreadable]